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Decisions in an oil refinery are made at three levels: planning, scheduling and control. Existing facilities have to be operated close to their maximum capacity, while continuously responding to cost fluctuations. In many of the currently reported planning models each decision level has its own model and is used separately. Integration of these models will lead to solutions closer to the optimum than modeling the decision levels separately. However, simply combining the levels into one model would lead to very large models that yet cannot be solved within reasonable time. To overcome the computational time issue a rolling horizon approach is proposed in this work.

Catechins are the main polyphenols present in a green tea and have been associated to several health benefits due to the antioxidant, antimutagenic and antiviral properties. They are, therefore, regarded as desired components with several applications in a variety of areas such as foods, cosmetics and pharmaceuticals. In this study a macroporous polymeric resin is used as an adsorption medium for separating the polyphenols. Based on experimentally determined mass transfer coefficients and diffusion coefficients, a packed bed column model is developed. The highlight of this work is that we are modeling a very complex multicomponent system, where the four catechins present in green tea and caffeine are competing for the adsorption sites. This model is an axial dispersed plug flow model where the mass transfer is modeled using the linear driving force (LDF) approach. This type of predictive models allows the estimation of the adsorption amount with the contact time. The model delivers catechins and caffeine breakthrough curves for the column and it can be used to optimize the parameters in such way that the highest possible recovery yield and purity for the catechins can be obtained.